hReg-CNCC

hReg-CNCC is a high-quality Regulatory network of Cranial Neural Crest Cell (CNCC), built by consensus optimization.

Consensen Network

Inputs

The inputs of consensus optimization are K (6) files of networks:
TF　TG　Sij　FDR　CRM; Seperated by tab
Examples are given in Input folder

Codes

python ConsOpt.py
cat ./Results/CNCCNetwork.txt | awk '{print $4}' | tr '_' '\t' | tr ';' '\n' | sortBed > ./Results/CNCCNetwork_RE1.txt
mergeBed -i ./Results/CNCCNetwork_RE1.txt > ./Results/CNCCNetwork_RE2.txt
bedtools intersect -wa -wb -a ./Results/CNCCNetwork_RE1.txt -b ./Results/CNCCNetwork_RE2.txt | awk '{print $1"_"$2"_"$3"\t"$4"_"$5"_"$6}' > ./Results/CNCCNetwork_RE1_RE2.txt

Annotating SNPs of Face GWAS

Input

The input of SNP annotation is GWAS summary statistics with p-value <= 1e-5:
chr　start　end　SNP_Name　p-value　Allele1　Allele2; Seperated by tab

Codes

bedtools intersect -wa -wb -a ./Input/FaceDisGWAS_e5.bed -b ./Results/CNCCNetwork_RE1.bed > FaceDisGWAS_SNP_RE.txt
for RE in `cat FaceDisGWAS_SNP_RE.txt | awk '{print $11}'`
do
  cat ./Results/CNCCNetwork.txt | grep $RE >> a
done
sort -k3nr a > FaceDisGWAS_Net_Sorted.txt; rm -f a;
python AnnoFaceGWAS.py

The output file is FaceDisGWAS_Net_Filtered.txt, which can be used for visualization and further analysis.

Annotating Human Ultraconserved Elements

Input

The input is bed file of Human Ultraconserved Elements:
chr　start　end; Seperated by tab

Codes

bedtools intersect -wa -wb -a ./Input/UltraConserved_hg19.bed -b ./Results/CNCCNetwork_RE1.bed > ./Results/ConservedRE_RE.txt
for RE in `cat ./Results/ConservedRE_RE.txt | awk '{print $7}'`
do
	cat ./Results/CNCCNetwork.txt | grep $RE >> a
done
cat a | sort | uniq > ./Results/ConservedRE_Net.txt;rm -f a
sort -k2 ./Results/ConservedRE_Net.txt > ConservedRE_Net_Sorted.txt
python AnnoConsElements.py

The output file is ConservedRE_Net_Filtered.txt, which can also be used for visualization and further analysis.

Requirements

python3
numpy
bedtools